Preliminary Annotation
2023-05-22
Intro and setup
Goal here is to validate the initial clustering and UMAP and ensure things like batch effect and clustering reflect the underlying biology.
#+ message = FALSE, warning = FALSE
suppressPackageStartupMessages(library(Seurat))
suppressPackageStartupMessages(library(EnsDb.Hsapiens.v86))
suppressPackageStartupMessages(library(BSgenome.Hsapiens.UCSC.hg38))
suppressPackageStartupMessages(library(Matrix))
suppressPackageStartupMessages(library(SeuratWrappers))
suppressPackageStartupMessages(library(harmony))
suppressPackageStartupMessages(library(ggplot2))
suppressPackageStartupMessages(library(dplyr))
setwd("~/gibbs/DOGMAMORPH/Ranalysis")
results<-readRDS( "Objects/20230518Completeobj.rds")Initial checks
First I am going to check there is no library or individual driven batch effect across the UMAP by visualizing on those. I’ll also visualize the treatment and timepoint to see if there are differences there that we’re picking up.
DimPlot(results, label = TRUE, reduction = "umap")## Loading required package: Signac
DimPlot(results, group.by="orig.ident", reduction = "umap")
DimPlot(results, group.by = "Participant", reduction = "umap")
DimPlot(results, group.by = "Treatment", reduction = "umap")
DimPlot(results, group.by = "Timepoint", reduction = "umap")
DimPlot(results, group.by = "Cell.Source", reduction = "umap")
Notes: Clustering - Overall looks good, appears that the substructure of outlying populations is reasonably separated by the clustering algorithm, although the mass of presumably CD4+ T cells are potentially overclustered as we can see several overlapping and presumably small clusters in that mass. Library - It appears there are no clusters driven entirely by library. There are differences in distribution but I suspect those are primarily due to PBMC vs CD4 libraries. Participant - There is clearly some variation, in participant that is not entirely corrected for (ex there is a patch of green that may be 5018 or something else along the bottom left of the large cluster). Overall this may be acceptbale variation, but after annotation I should check that there are not too much participant - to - participant variability in cluster membership Treatment - Minor treatment driven differences but overall appears well mixed Time point - Appears to be some outlying populations that are potentially perturbed, but it’s overall looking well mixed. Cell source - looks like that main cluster is CD4, with a sub cluster pulled out to the left and to the bottom. CD4 also participates in other clusters, likely due to low levels of contamination.
Also checking here to see if there are any QC related factors that might be driving some of the cluster distribution:
FeaturePlot(results, "mt", reduction = "umap")
FeaturePlot(results, "nCount_RNA", reduction = "umap")
FeaturePlot(results,"nFeature_RNA", reduction = "umap")
FeaturePlot(results,"nCount_ATAC", reduction = "umap")
FeaturePlot(results,"nFeature_ATAC", reduction = "umap")
FeaturePlot(results,"nFeature_ADT.dsb", reduction = "umap")
FeaturePlot(results,"nFeature_ADT.dsb", reduction = "umap")
No clusters appear overwhelmingly driven by a QC metric. A few on the periphery have high RNA/ATAC counts/features but nothing too abnormal
Next I’ll just briefly look to see if there are quality differences across samples
group_by(results@meta.data, Participant)%>%summarise(Cell_count=n(), Median_genes=median(nFeature_RNA), Median_RNA_UMI=median(nCount_RNA), Median_ATAC_Fragments=median(nCount_ATAC), Median_ADT_UMI=median(nCount_ADT))## # A tibble: 12 x 6
## Participant Cell_count Median_genes Median_RNA_UMI Median_ATAC_Fragments
## <int> <int> <dbl> <dbl> <dbl>
## 1 5003 909 1326 2503 4593
## 2 5009 1425 1643 3328 5032
## 3 5014 2942 1520. 3095 4794.
## 4 5016 1864 1469 2885 3992
## 5 5017 2288 1599 3116. 4640.
## 6 5018 6414 1300 2299 3639
## 7 5020 1708 1363 2540. 3648
## 8 5021 931 1459 2817 4856
## 9 5023 1833 1539 3029 5034
## 10 5026 4236 1504. 3038 4364.
## 11 5035 2564 1438. 2854. 4232
## 12 5037 2348 1472. 2791 5070.
## # i 1 more variable: Median_ADT_UMI <dbl>group_by(results@meta.data, Participant, Timepoint)%>%summarise(Cell_count=n(), Median_genes=median(nFeature_RNA), Median_RNA_UMI=median(nCount_RNA), Median_ATAC_Fragments=median(nCount_ATAC), Median_ADT_UMI=median(nCount_ADT))%>%print(n=10000)## `summarise()` has grouped output by 'Participant'. You can override using the
## `.groups` argument.## # A tibble: 24 x 7
## # Groups: Participant [12]
## Participant Timepoint Cell_count Median_genes Median_RNA_UMI
## <int> <int> <int> <dbl> <dbl>
## 1 5003 0 415 1379 2620
## 2 5003 3 494 1285 2386.
## 3 5009 0 342 1350 2558.
## 4 5009 3 1083 1745 3622
## 5 5014 0 1127 1470 2928
## 6 5014 3 1815 1543 3186
## 7 5016 0 851 1487 2933
## 8 5016 3 1013 1451 2847
## 9 5017 0 563 1594 3076
## 10 5017 3 1725 1606 3131
## 11 5018 0 5451 1238 2136
## 12 5018 3 963 1623 3167
## 13 5020 0 832 1363 2528.
## 14 5020 3 876 1364. 2580.
## 15 5021 0 355 1469 2957
## 16 5021 3 576 1435 2767
## 17 5023 0 1154 1502. 2936.
## 18 5023 3 679 1601 3209
## 19 5026 0 3475 1475 2986
## 20 5026 3 761 1618 3434
## 21 5035 0 1411 1377 2699
## 22 5035 3 1153 1527 3098
## 23 5037 0 1651 1403 2623
## 24 5037 3 697 1843 3771
## # i 2 more variables: Median_ATAC_Fragments <dbl>, Median_ADT_UMI <dbl>group_by(results@meta.data, Treatment, Timepoint)%>%summarise(Cell_count=n(), Median_genes=median(nFeature_RNA), Median_RNA_UMI=median(nCount_RNA), Median_ATAC_Fragments=median(nCount_ATAC), Median_ADT_UMI=median(nCount_ADT))## `summarise()` has grouped output by 'Treatment'. You can override using the
## `.groups` argument.## # A tibble: 6 x 7
## # Groups: Treatment [3]
## Treatment Timepoint Cell_count Median_genes Median_RNA_UMI
## <chr> <int> <int> <dbl> <dbl>
## 1 Bup.Nalo 0 8295 1351 2497
## 2 Bup.Nalo 3 5182 1582 3165
## 3 Methadone 0 2440 1415 2698.
## 4 Methadone 3 3466 1449 2816
## 5 Naltrexone 0 6892 1439 2846
## 6 Naltrexone 3 3187 1574 3199
## # i 2 more variables: Median_ATAC_Fragments <dbl>, Median_ADT_UMI <dbl>Gene Expression overlays
Goal of this section is to use a few known markers to very roughly annotate the cells. I’ll use several sources for these. One is the Seurat tutorial, which is mainly RNA markers: Cluster ID Markers Cell Type IL7R, CCR7 Naive CD4+ T CD14, LYZ CD14+ Mono IL7R, S100A4 Memory CD4+ MS4A1 B CD8A CD8+ T FCGR3A, MS4A7 FCGR3A+ Mono GNLY, NKG7 NK FCER1A, CST3 DC PPBP Platelet
pbmc_marks_Seurat<-c("IL7R","CCR7","CD14","LYZ","S100A4","MS4A1","CD8A","FCGR3A","MS4A7","GNLY","NKG7","FCER1A","CST3","PPBP")
DefaultAssay(results)<-"RNA"
results<-ScaleData(results, features = pbmc_marks_Seurat)## Centering and scaling data matrixFeaturePlot(results, pbmc_marks_Seurat, ncol = 4)
DotPlot(results, features = pbmc_marks_Seurat)+theme(axis.text.x = element_text(vjust = 0.7, angle = 90))
VlnPlot(results, features = pbmc_marks_Seurat)+theme(axis.text.x = element_text(vjust = 0.7, angle = 90))
DoHeatmap(results, pbmc_marks_Seurat)
results<-ScaleData(results, features = pbmc_marks_Seurat, split.by = "orig.ident")## Centering and scaling data matrix## Centering and scaling data from split JC10## Centering and scaling data from split JC11## Centering and scaling data from split JC12## Centering and scaling data from split JC4## Centering and scaling data from split JC5## Centering and scaling data from split JC6## Centering and scaling data from split JC7## Centering and scaling data from split JC8## Centering and scaling data from split JC9DoHeatmap(results, pbmc_marks_Seurat, disp.min = -2, disp.max = 2)
Observations - The heatmap looks somewhat different from what I’m used to on account of it being a mixed population without expression of most markers in most cell types (as opposed to the typical CD4 or the DE defined markers). Based on this, I would annotate the first 15 clusters as : 0- Naive CD4+ 1- Naive CD4+ 2- Mix of Naive and Memory CD4+ 3- Memory CD4+ 4- Memory CD4+/CD8/NK 5- B cells 6- Naive CD4 7-Memory CD4+ 8-Naive CD4+ 9-NK 10-B cells 11-CD14+ Mono 12-DC 13-FCGR3A+ Mono 14- Memory CD4+
(no platelets in our data)
Another is the clonal replacement paper for Howard Chang (Yost et al. 2019), also RNA: Relevant method Excerpt: >including CD3G, CD3D, CD3E, CD2 (T cells), CD8A, GZMA (CD8+ T cells), CD4, FOXP3 (CD4+ T cells and Treg cells), KLRC1, KLRC3 (NK cells), CD19, CD79A (B cells), SLAMF7, IGKC (plasma cells), FCGR2A, CSF1R (macrophages), FLT3 (dendritic cells), CLEC4C (plasmacytoid dendritic cells)
pbmc_marks_Chang<-c("CD3G", "CD3D", "CD3E", "CD2","CD8A", "GZMA","CD4", "FOXP3","KLRC1", "KLRC3","CD19", "CD79A","SLAMF7", "IGKC" ,"FCGR2A", "CSF1R","FLT3","CLEC4C")
DefaultAssay(results)<-"RNA"
results<-ScaleData(results, features = pbmc_marks_Chang)## Centering and scaling data matrixFeaturePlot(results, pbmc_marks_Chang, ncol = 4)
DotPlot(results, features = pbmc_marks_Chang)+theme(axis.text.x = element_text(vjust = 0.7, angle = 90))
VlnPlot(results, features = pbmc_marks_Chang)+theme(axis.text.x = element_text(vjust = 0.7, angle = 90))
DoHeatmap(results, pbmc_marks_Chang)
results<-ScaleData(results, features = pbmc_marks_Chang, split.by = "orig.ident")## Centering and scaling data matrix## Centering and scaling data from split JC10## Centering and scaling data from split JC11## Centering and scaling data from split JC12## Centering and scaling data from split JC4## Centering and scaling data from split JC5## Centering and scaling data from split JC6## Centering and scaling data from split JC7## Centering and scaling data from split JC8## Centering and scaling data from split JC9DoHeatmap(results, pbmc_marks_Chang, disp.min = -2, disp.max = 2)
Observations - The heatmap looks somewhat different from what I’m used to on account of it being a mixed population without expression of most markers in most cell types (as opposed to the typical CD4 or the DE defined markers). Based on this, I would annotate the first 15 clusters as : 0- T cells 1- T cells 2- T cells 3- T cells 4- T cells/CD8 5- B cells/Plasma 6- T cells 7- T cells/TREG 8- T cells 9- NK 10- B cells/Plasma 11-macrophages/DCs 12-macrophages 13-macrophages 14- ?
- SLAMF7 and IGKC were discordant. Annotating plasma cells based on just IGKC, appears 15 might be the true plasma cells
- No cluster marked for CLEC4C, pDCs are rare so it might make sense we dont pick them up as a separate cluster, possibly they are a part of cluster 12
Finally we have those that I picked for CD4 more specifically in the immunity paper (Collora et al. 2022), RNA and protein:
Cluster Transcriptomic marker Surface protein marker positive GZMB Th1 GZMB, CCL5, TBX21 CD45RO, HLA DR GZMK Th1 TBX21, GZMK CD45RO, IL7R, CXCR3, CCR6, CD69 Th2 GATA3 CD45RO, IL7R, CCR4, CD69 Th17 RORC, CTSH, CCR6 CD45RO, IL7R, CCR6, TIGIT Treg FOXP3, IL2RA CD45RO, TIGIT, CD25, HLA DR Proliferating MKI67 CD45RO, IL7R, CXCR3, CCR6, CD40L, CD137, PD1, TIGIT, HLA-DR CXCR5 Memory CXCR5 CD45RO Memory CD45RO, PD1 Naïve CCR7, SELL, TCF7 CD45RA, IL7R MT Percent mitochondria genes CD45RO
CD4_marks_Collora_RNA<-c("GZMB", "CCL5", "TBX21", "GZMK","GATA3","RORC", "CTSH","FOXP3", "IL2RA","MKI67", "CXCR5", "CCR7", "SELL", "TCF7", "mt")
DefaultAssay(results)<-"RNA"
results<-ScaleData(results, features = CD4_marks_Collora_RNA)## Centering and scaling data matrixFeaturePlot(results, CD4_marks_Collora_RNA, ncol = 4)
DotPlot(results, features = CD4_marks_Collora_RNA) +theme(axis.text.x = element_text(vjust = 0.7, angle = 90))
VlnPlot(results, features = CD4_marks_Collora_RNA) +theme(axis.text.x = element_text(vjust = 0.7, angle = 90))
DoHeatmap(results, CD4_marks_Collora_RNA[1:14])
results<-ScaleData(results, features = CD4_marks_Collora_RNA, split.by = "orig.ident")## Centering and scaling data matrix## Centering and scaling data from split JC10## Centering and scaling data from split JC11## Centering and scaling data from split JC12## Centering and scaling data from split JC4## Centering and scaling data from split JC5## Centering and scaling data from split JC6## Centering and scaling data from split JC7## Centering and scaling data from split JC8## Centering and scaling data from split JC9DoHeatmap(results, CD4_marks_Collora_RNA[1:14], disp.min = -2, disp.max = 2)
CD4_marks_Collora_protein<-c("CD45RA-TotalA", "CD45RO-TotalA", "HLA-DR-DP-DQ-TotalA", "CD127-TotalA","CD183-TotalA","CD196-TotalA","CD194-TotalA","TIGIT-TotalA","CD25-TotalA", "CD154-TotalA", "CD137-TotalA", "CD279-TotalA")
DefaultAssay(results)<-"ADT.dsb"
results<-ScaleData(results, features = CD4_marks_Collora_protein)## Centering and scaling data matrixFeaturePlot(results, CD4_marks_Collora_protein, ncol = 4, reduction = "umap", min.cutoff = 'q5', max.cutoff = 'q95')
DotPlot(results, features = CD4_marks_Collora_protein, scale.min=0, scale.max=100)+theme(axis.text.x = element_text(vjust = 0.7, angle = 90))
VlnPlot(results, features = CD4_marks_Collora_protein)+theme(axis.text.x = element_text(vjust = 0.7, angle = 90))
DoHeatmap(results, CD4_marks_Collora_protein)
Observations and rough annotation, note these markers are for CD4 T
cells so we’re probably not going to be able to annotate clusters other
than 0-8 (minus 5) RNA alone- 0- Naive/Memory CD4 1- Naive/Memory CD4 2-
Naive/Memory CD4 3- Naive/Memory CD4 4- GZMB+ TH1 5- 6- Naive CD4 7-
Treg, proliferating 8- Memory CD4 9- GZMB+ TH1 10- 11- 12- 13- 14-
Protein alone- 0- RA positive 1- RO positive IL7R positive CXCR3
Positive CCR6 Positive CCR4 Positive TIGIT Positive CD25 positive CD154
Positive CD137 Positive CD279 Positive 2- RO positive IL7R positive
CXCR3 Positive CD154 Positive CD137 Positive 3- RO positive IL7R
positive CXCR3 Positive 4- RO positive 5- DR Positive, RA positive 6- RA
positive 7- DR Positive RO positive, CD25 positive 8- CD137 Positive 9-
RA positive 10- DR Positive 11- 12- DR Positive 13- 14-
Composite annotation (Collora markers)- 0- Naive CD4 T cells 1- Memory CD4, effector polarized 2- Memory CD4, TFh polarized? 3- Memory CD4 4- Memory CD4 5- ? 6- Naive CD4 7- Treg, Activated cells 8- ? 9- Effector/GZMB Th1 10- 11- 12- 13- 14-
Final initial clustering across markers for the first 15 clusters
This is the annotation I came up with for the first 15 clusters based on the markers above.
0- Naive CD4 T cells 1- Memory CD4, effector polarized 2- Memory CD4, TFh polarized? 3- Memory CD4 4- Memory CD4 Th1 GZMB+ 5- B cells 6- Naive CD4 7- Treg, Activated cells 8- Naive T cells 9- Effector/GZMB Th1/ NK 10-B cells 11-CD14+ Mono 12-DC 13-FCGR3A+ Mono 14-Memory CD4+
Some initial observations: The remaining clusters are small and likely cna be merged into other clusters based on some criteria. 7 Possibly is comprized of T regs and proliferating cells. 4/9 is the cytotoxic section We’re currently under utilizing the protein markers for annotation, but further annotation will help with that. 1 and 2 appear to be merging across several clusters including subsets that are PD1 positive, that have different effector profiles, etc. We will likely need to do a separate clustering focused on specific subsets to parse the heterogeneity here.
devtools::session_info()## Warning in system("timedatectl", intern = TRUE): running command 'timedatectl'
## had status 1## - Session info ---------------------------------------------------------------
## setting value
## version R version 4.2.0 (2022-04-22)
## os Red Hat Enterprise Linux 8.7 (Ootpa)
## system x86_64, linux-gnu
## ui X11
## language (EN)
## collate C
## ctype C
## tz Etc/UTC
## date 2023-05-25
## pandoc 2.17.1.1 @ /usr/lib/rstudio-server/bin/quarto/bin/ (via rmarkdown)
##
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## pkgbuild 1.4.0 2022-11-27 [1] CRAN (R 4.2.0)
## pkgconfig 2.0.3 2019-09-22 [2] CRAN (R 4.2.0)
## pkgload 1.3.2 2022-11-16 [1] CRAN (R 4.2.0)
## plotly 4.10.1 2022-11-07 [1] CRAN (R 4.2.0)
## plyr 1.8.8 2022-11-11 [1] CRAN (R 4.2.0)
## png 0.1-8 2022-11-29 [1] CRAN (R 4.2.0)
## polyclip 1.10-4 2022-10-20 [1] CRAN (R 4.2.0)
## prettyunits 1.1.1 2020-01-24 [2] CRAN (R 4.2.0)
## processx 3.8.1 2023-04-18 [1] CRAN (R 4.2.0)
## profvis 0.3.8 2023-05-02 [1] CRAN (R 4.2.0)
## progress 1.2.2 2019-05-16 [2] CRAN (R 4.2.0)
## progressr 0.13.0 2023-01-10 [1] CRAN (R 4.2.0)
## promises 1.2.0.1 2021-02-11 [2] CRAN (R 4.2.0)
## ProtGenerics 1.30.0 2022-11-01 [1] Bioconductor
## ps 1.7.5 2023-04-18 [1] CRAN (R 4.2.0)
## purrr 1.0.1 2023-01-10 [1] CRAN (R 4.2.0)
## R.methodsS3 1.8.2 2022-06-13 [1] CRAN (R 4.2.0)
## R.oo 1.25.0 2022-06-12 [1] CRAN (R 4.2.0)
## R.utils 2.12.2 2022-11-11 [1] CRAN (R 4.2.0)
## R6 2.5.1 2021-08-19 [2] CRAN (R 4.2.0)
## RANN 2.6.1 2019-01-08 [2] CRAN (R 4.2.0)
## rappdirs 0.3.3 2021-01-31 [2] CRAN (R 4.2.0)
## RColorBrewer 1.1-3 2022-04-03 [2] CRAN (R 4.2.0)
## Rcpp * 1.0.10 2023-01-22 [1] CRAN (R 4.2.0)
## RcppAnnoy 0.0.20 2022-10-27 [1] CRAN (R 4.2.0)
## RcppRoll 0.3.0 2018-06-05 [2] CRAN (R 4.2.0)
## RCurl 1.98-1.12 2023-03-27 [1] CRAN (R 4.2.0)
## remotes 2.4.2 2021-11-30 [2] CRAN (R 4.2.0)
## reshape2 1.4.4 2020-04-09 [2] CRAN (R 4.2.0)
## restfulr 0.0.15 2022-06-16 [1] CRAN (R 4.2.0)
## reticulate 1.28 2023-01-27 [1] CRAN (R 4.2.0)
## rjson 0.2.21 2022-01-09 [2] CRAN (R 4.2.0)
## rlang 1.1.1 2023-04-28 [1] CRAN (R 4.2.0)
## rmarkdown 2.21 2023-03-26 [1] CRAN (R 4.2.0)
## ROCR 1.0-11 2020-05-02 [2] CRAN (R 4.2.0)
## Rsamtools 2.14.0 2022-11-01 [1] Bioconductor
## RSQLite 2.3.1 2023-04-03 [1] CRAN (R 4.2.0)
## rstudioapi 0.14 2022-08-22 [1] CRAN (R 4.2.0)
## rsvd 1.0.5 2021-04-16 [1] CRAN (R 4.2.0)
## rtracklayer * 1.58.0 2022-11-01 [1] Bioconductor
## Rtsne 0.16 2022-04-17 [2] CRAN (R 4.2.0)
## S4Vectors * 0.36.2 2023-02-26 [1] Bioconductor
## sass 0.4.5 2023-01-24 [1] CRAN (R 4.2.0)
## scales 1.2.1 2022-08-20 [1] CRAN (R 4.2.0)
## scattermore 0.8 2022-02-14 [1] CRAN (R 4.2.0)
## sctransform 0.3.5 2022-09-21 [1] CRAN (R 4.2.0)
## sessioninfo 1.2.2 2021-12-06 [2] CRAN (R 4.2.0)
## Seurat * 4.3.0 2022-11-18 [1] CRAN (R 4.2.0)
## SeuratObject * 4.1.3 2022-11-07 [1] CRAN (R 4.2.0)
## SeuratWrappers * 0.3.1 2023-03-17 [1] Github (satijalab/seurat-wrappers@d28512f)
## shiny 1.7.4 2022-12-15 [1] CRAN (R 4.2.0)
## Signac * 1.9.0 2022-12-08 [1] CRAN (R 4.2.0)
## sp 1.6-0 2023-01-19 [1] CRAN (R 4.2.0)
## spatstat.data 3.0-1 2023-03-12 [1] CRAN (R 4.2.0)
## spatstat.explore 3.1-0 2023-03-14 [1] CRAN (R 4.2.0)
## spatstat.geom 3.1-0 2023-03-12 [1] CRAN (R 4.2.0)
## spatstat.random 3.1-4 2023-03-13 [1] CRAN (R 4.2.0)
## spatstat.sparse 3.0-1 2023-03-12 [1] CRAN (R 4.2.0)
## spatstat.utils 3.0-2 2023-03-11 [1] CRAN (R 4.2.0)
## stringi 1.7.12 2023-01-11 [1] CRAN (R 4.2.0)
## stringr 1.5.0 2022-12-02 [1] CRAN (R 4.2.0)
## SummarizedExperiment 1.28.0 2022-11-01 [1] Bioconductor
## survival 3.5-5 2023-03-12 [1] CRAN (R 4.2.0)
## tensor 1.5 2012-05-05 [2] CRAN (R 4.2.0)
## tibble 3.2.1 2023-03-20 [1] CRAN (R 4.2.0)
## tidyr 1.3.0 2023-01-24 [1] CRAN (R 4.2.0)
## tidyselect 1.2.0 2022-10-10 [1] CRAN (R 4.2.0)
## urlchecker 1.0.1 2021-11-30 [1] CRAN (R 4.2.0)
## usethis 2.1.6 2022-05-25 [1] CRAN (R 4.2.0)
## utf8 1.2.3 2023-01-31 [1] CRAN (R 4.2.0)
## uwot 0.1.14 2022-08-22 [1] CRAN (R 4.2.0)
## vctrs 0.6.2 2023-04-19 [1] CRAN (R 4.2.0)
## vipor 0.4.5 2017-03-22 [2] CRAN (R 4.2.0)
## viridisLite 0.4.2 2023-05-02 [1] CRAN (R 4.2.0)
## withr 2.5.0 2022-03-03 [2] CRAN (R 4.2.0)
## xfun 0.39 2023-04-20 [1] CRAN (R 4.2.0)
## XML 3.99-0.14 2023-03-19 [1] CRAN (R 4.2.0)
## xml2 1.3.3 2021-11-30 [2] CRAN (R 4.2.0)
## xtable 1.8-4 2019-04-21 [2] CRAN (R 4.2.0)
## XVector * 0.38.0 2022-11-01 [1] Bioconductor
## yaml 2.3.7 2023-01-23 [1] CRAN (R 4.2.0)
## zlibbioc 1.44.0 2022-11-01 [1] Bioconductor
## zoo 1.8-12 2023-04-13 [1] CRAN (R 4.2.0)
##
## [1] /gpfs/gibbs/project/ya-chi_ho/jac369/R/4.2
## [2] /vast/palmer/apps/avx2/software/R/4.2.0-foss-2020b/lib64/R/library
##
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